autolens.AnalysisPoint#

class AnalysisPoint[source]#

Bases: Analysis, AnalysisLens

Fits a lens model to a point source dataset (e.g. positions, fluxes, time delays) via a non-linear search.

The Analysis class defines the log_likelihood_function which fits the model to the dataset and returns the log likelihood value defining how well the model fitted the data.

It handles many other tasks, such as visualization, outputting results to hard-disk and storing results in a format that can be loaded after the model-fit is complete.

This class is used for model-fits which fit lens models to point datasets, which may include some combination of positions, fluxes and time-delays.

This class stores the settings used to perform the model-fit for certain components of the model (e.g. a pixelization or inversion), the Cosmology used for the analysis and adapt images used for certain model classes.

Parameters:

dataset (PointDataset) – The PointDataset that is fitted by the model, which contains a combination of positions, fluxes and time-delays.
solver (PointSolver) – Solves the lens equation in order to determine the image-plane positions of a point source by ray-tracing triangles to and from the source-plane.
fit_positions_cls – The class used to fit the positions of the point source dataset, which could be an image-plane or source-plane chi-squared.
cosmology (LensingCosmology) – The Cosmology assumed for this analysis.
title_prefix (str) – A string that is added before the title of all figures output by visualization, for example to put the name of the dataset and galaxy in the title.

Methods

`compute_latent_samples`	Compute latent variables from a model instance.
`compute_latent_variables`	Override to compute latent variables from the instance.
`dataset_model_via_instance_from`	Create a dataset model from a model instance, which is used to fit the dataset.
`fit_for_visualization`	Build the fit used by the visualizer.
`fit_from`	Given a model instance create a FitPointDataset object.
`galaxies_via_instance_from`	Create a list of galaxies from a model instance, which is used to fit the dataset.
`log_likelihood_function`	Given an instance of the model, where the model parameters are set via a non-linear search, fit the model instance to the point source dataset.
`log_likelihood_penalty_from`	Call the positions overwrite log likelihood function, which add a penalty term to the likelihood if the positions of the multiple images of the lensed source do not trace close to one another in the source plane.
`make_result`	After the non-linear search is complete create its Result, which includes:
`modify_after_fit`	Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.
`modify_before_fit`	Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.
`modify_model`
`perform_quick_update`	Perform a quick visualization update during non-linear search fitting.
`print_vram_use`	Print JAX VRAM use for a given batch size.
`save_attributes`	Before the non-linear search begins, this routine saves attributes of the Analysis object to the files folder such that they can be loaded after the analysis using PyAutoFit's database and aggregator tools.
`save_results`
`save_results_combined`
`tracer_via_instance_from`	Create a Tracer from the galaxies contained in a model instance.
`with_model`	Associate an explicit model with this analysis.

Attributes

`LATENT_BATCH_MODE`
`LATENT_KEYS`
`supports_background_update`	Whether this analysis supports background quick updates.
`supports_jax_visualization`	Whether the visualizer can work directly with JAX arrays.

Visualizer#: alias of VisualizerPoint

Result#: alias of ResultPoint

log_likelihood_function(instance)[source]#

Given an instance of the model, where the model parameters are set via a non-linear search, fit the model instance to the point source dataset.

This function returns a log likelihood which is used by the non-linear search to guide the model-fit.

For this analysis class, this function performs the following steps:

Extracts all galaxies from the model instance and set up a Tracer, which includes ordering the galaxies by redshift to set up each Plane.
Use the Tracer and other attributes to create a FitPointDataset object, which performs the steps below to fit different components of the point source dataset.
If the point source dataset has positions and model fits positions, perform this fit and compute the log likelihood. This calculation uses the fit_positions_cls object, which may be an image-plane or source-plane chi-squared.
If the point source dataset has fluxes and model fits fluxes, perform this fit and compute the log likelihood.
If the point source dataset has time-delays and model fits time-delays, perform this fit and compute the log likelihood [NOT SUPPORTED YET].
Sum the log likelihoods of the positions, fluxes and time-delays (if they are fitted) to get the overall log likelihood of the model.

Certain models will fail to fit the dataset and raise an exception. For example for ill defined mass models the PointSolver may find no solution. In such circumstances the model is discarded and its likelihood value is passed to the non-linear search in a way that it ignores it (for example, using a value of -1.0e99).

Parameters:: instance – An instance of the model that is being fitted to the data by this analysis (whose parameters have been set via a non-linear search).
Returns:: The log likelihood indicating how well this model instance fitted the imaging data.
Return type:: float

fit_from(instance)[source]#

Given a model instance create a FitPointDataset object.

This function is used in the log_likelihood_function to fit the model to the imaging data and compute the log likelihood.

Parameters:: instance – An instance of the model that is being fitted to the data by this analysis (whose parameters have been set via a non-linear search).
Return type:: The fit of the lens model to the point source dataset.

save_attributes(paths)[source]#

Before the non-linear search begins, this routine saves attributes of the Analysis object to the files folder such that they can be loaded after the analysis using PyAutoFit’s database and aggregator tools.

For this analysis, it uses the AnalysisDataset object’s method to output the following:

The dataset’s point source dataset as a readable .json file.

It is common for these attributes to be loaded by many of the template aggregator functions given in the aggregator modules. For example, when using the database tools to perform a fit, the default behaviour is for the dataset, settings and other attributes necessary to perform the fit to be loaded via the pickle files output by this function.

Parameters:: paths (DirectoryPaths) – The paths object which manages all paths, e.g. where the non-linear search outputs are stored, visualization, and the pickled objects used by the aggregator output by this function.